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Wu L, Hwang SF, Strelkov SE, Fredua-Agyeman R, Oh SH, Bélanger RR, Wally O, Kim YM. Pathogenicity, Host Resistance, and Genetic Diversity of Fusarium Species under Controlled Conditions from Soybean in Canada. J Fungi (Basel) 2024; 10:303. [PMID: 38786658 PMCID: PMC11122035 DOI: 10.3390/jof10050303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/26/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
Fusarium spp. are commonly associated with the root rot complex of soybean (Glycine max). Previous surveys identified six common Fusarium species from Manitoba, including F. oxysporum, F. redolens, F. graminearum, F. solani, F. avenaceum, and F. acuminatum. This study aimed to determine their pathogenicity, assess host resistance, and evaluate the genetic diversity of Fusarium spp. isolated from Canada. The pathogenicity of these species was tested on two soybean cultivars, 'Akras' (moderately resistant) and 'B150Y1' (susceptible), under greenhouse conditions. The aggressiveness of the fungal isolates varied, with root rot severities ranging from 1.5 to 3.3 on a 0-4 scale. Subsequently, the six species were used to screen a panel of 20 Canadian soybean cultivars for resistance in a greenhouse. Cluster and principal component analyses were conducted based on the same traits used in the pathogenicity study. Two cultivars, 'P15T46R2' and 'B150Y1', were consistently found to be tolerant to F. oxysporum, F. redolens, F. graminearum, and F. solani. To investigate the incidence and prevalence of Fusarium spp. in Canada, fungi were isolated from 106 soybean fields surveyed across Manitoba, Saskatchewan, Ontario, and Quebec. Eighty-three Fusarium isolates were evaluated based on morphology and with multiple PCR primers, and phylogenetic analyses indicated their diversity across the major soybean production regions of Canada. Overall, this study contributes valuable insights into host resistance and the pathogenicity and genetic diversity of Fusarium spp. in Canadian soybean fields.
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Affiliation(s)
- Longfei Wu
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (L.W.); (S.-F.H.); (S.E.S.); (R.F.-A.); (S.-H.O.)
| | - Sheau-Fang Hwang
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (L.W.); (S.-F.H.); (S.E.S.); (R.F.-A.); (S.-H.O.)
| | - Stephen E. Strelkov
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (L.W.); (S.-F.H.); (S.E.S.); (R.F.-A.); (S.-H.O.)
| | - Rudolph Fredua-Agyeman
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (L.W.); (S.-F.H.); (S.E.S.); (R.F.-A.); (S.-H.O.)
| | - Sang-Heon Oh
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (L.W.); (S.-F.H.); (S.E.S.); (R.F.-A.); (S.-H.O.)
| | - Richard R. Bélanger
- Centre de Recherche en Innovation des Végétaux, Université Laval, Québec, QC G1V 0A6, Canada;
| | - Owen Wally
- Harrow Research and Development Centre, Agriculture and Agri-Food Canada, Harrow, ON N0R 1G0, Canada;
| | - Yong-Min Kim
- Brandon Research and Development Centre, Agriculture and Agri-Food Canada, Brandon, MB R7C 5Y3, Canada
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Shrestha AMS, Gonzales MEM, Ong PCL, Larmande P, Lee HS, Jeung JU, Kohli A, Chebotarov D, Mauleon RP, Lee JS, McNally KL. RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci. Gigascience 2024; 13:giae013. [PMID: 38832465 PMCID: PMC11148593 DOI: 10.1093/gigascience/giae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 02/21/2024] [Accepted: 03/12/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources. RESULTS We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs. CONCLUSIONS RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.
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Affiliation(s)
- Anish M S Shrestha
- Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, College of Computer Studies, De La Salle University, Manila 1004, Philippines
- International Rice Research Institute (IRRI), Metro Manila 1301, Philippines
| | - Mark Edward M Gonzales
- Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, College of Computer Studies, De La Salle University, Manila 1004, Philippines
| | - Phoebe Clare L Ong
- Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, College of Computer Studies, De La Salle University, Manila 1004, Philippines
| | - Pierre Larmande
- DIADE, Univ Montpellier, Cirad, IRD, 34394 Montpellier, France
| | - Hyun-Sook Lee
- National Institute of Crop Science, Wanju-gun 55365, Republic of Korea
| | - Ji-Ung Jeung
- National Institute of Crop Science, Wanju-gun 55365, Republic of Korea
| | - Ajay Kohli
- International Rice Research Institute (IRRI), Metro Manila 1301, Philippines
| | - Dmytro Chebotarov
- International Rice Research Institute (IRRI), Metro Manila 1301, Philippines
| | - Ramil P Mauleon
- International Rice Research Institute (IRRI), Metro Manila 1301, Philippines
| | - Jae-Sung Lee
- International Rice Research Institute (IRRI), Metro Manila 1301, Philippines
| | - Kenneth L McNally
- International Rice Research Institute (IRRI), Metro Manila 1301, Philippines
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Almeida-Silva F, Pedrosa-Silva F, Venancio TM. The Soybean Expression Atlas v2: A comprehensive database of over 5000 RNA-seq samples. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:1041-1051. [PMID: 37681739 DOI: 10.1111/tpj.16459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/04/2023] [Accepted: 08/28/2023] [Indexed: 09/09/2023]
Abstract
Soybean is a crucial crop worldwide, used as a source of food, feed, and industrial products due to its high protein and oil content. Previously, the rapid accumulation of soybean RNA-seq data in public databases and the computational challenges of processing raw RNA-seq data motivated us to develop the Soybean Expression Atlas, a gene expression database of over a thousand RNA-seq samples. Over the past few years, our database has allowed researchers to explore the expression profiles of important gene families, discover genes associated with agronomic traits, and understand the transcriptional dynamics of cellular processes. Here, we present the Soybean Expression Atlas v2, an updated version of our database with a fourfold increase in the number of samples, featuring transcript- and gene-level transcript abundance matrices for 5481 publicly available RNA-seq samples. New features in our database include the availability of transcript-level abundance estimates and equivalence classes to explore differential transcript usage, abundance estimates in bias-corrected counts to increase the accuracy of differential gene expression analyses, a new web interface with improved data visualization and user experience, and a reproducible and scalable pipeline available as an R package. The Soybean Expression Atlas v2 is available at https://soyatlas.venanciogroup.uenf.br/, and it will accelerate soybean research, empowering researchers with high-quality and easily accessible gene expression data.
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Affiliation(s)
- Fabricio Almeida-Silva
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium
- VIB Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
| | - Francisnei Pedrosa-Silva
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Brazil
| | - Thiago M Venancio
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Brazil
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Krishnan P, Caseys C, Soltis N, Zhang W, Burow M, Kliebenstein DJ. Polygenic pathogen networks influence transcriptional plasticity in the Arabidopsis-Botrytis pathosystem. Genetics 2023; 224:iyad099. [PMID: 37216906 PMCID: PMC10789313 DOI: 10.1093/genetics/iyad099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 03/30/2023] [Accepted: 05/16/2023] [Indexed: 05/24/2023] Open
Abstract
Bidirectional flow of information shapes the outcome of the host-pathogen interactions and depends on the genetics of each organism. Recent work has begun to use co-transcriptomic studies to shed light on this bidirectional flow, but it is unclear how plastic the co-transcriptome is in response to genetic variation in both the host and pathogen. To study co-transcriptome plasticity, we conducted transcriptomics using natural genetic variation in the pathogen, Botrytis cinerea, and large-effect genetic variation abolishing defense signaling pathways within the host, Arabidopsis thaliana. We show that genetic variation in the pathogen has a greater influence on the co-transcriptome than mutations that abolish defense signaling pathways in the host. Genome-wide association mapping using the pathogens' genetic variation and both organisms' transcriptomes allowed an assessment of how the pathogen modulates plasticity in response to the host. This showed that the differences in both organism's responses were linked to trans-expression quantitative trait loci (eQTL) hotspots within the pathogen's genome. These hotspots control gene sets in either the host or pathogen and show differential allele sensitivity to the host's genetic variation rather than qualitative host specificity. Interestingly, nearly all the trans-eQTL hotspots were unique to the host or pathogen transcriptomes. In this system of differential plasticity, the pathogen mediates the shift in the co-transcriptome more than the host.
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Affiliation(s)
- Parvathy Krishnan
- DynaMo Center of Excellence, University of Copenhagen, Copenhagen DL-1165Denmark
| | - Celine Caseys
- Department of Plant Sciences, University of California Davis, Davis, CA 95616USA
| | - Nik Soltis
- Department of Plant Sciences, University of California Davis, Davis, CA 95616USA
| | - Wei Zhang
- Department of Botany & Plant Sciences, Institute for Integrative Genome Biology, University of California Riverside, Riverside, CA 92521, USA
| | - Meike Burow
- DynaMo Center of Excellence, University of Copenhagen, Copenhagen DL-1165Denmark
| | - Daniel J Kliebenstein
- DynaMo Center of Excellence, University of Copenhagen, Copenhagen DL-1165Denmark
- Department of Plant Sciences, University of California Davis, Davis, CA 95616USA
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Discovering and prioritizing candidate resistance genes against soybean pests by integrating GWAS and gene coexpression networks. Gene 2023; 860:147231. [PMID: 36731618 DOI: 10.1016/j.gene.2023.147231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 01/16/2023] [Accepted: 01/25/2023] [Indexed: 02/02/2023]
Abstract
Soybean is one of the most important legume crops worldwide. Soybean pests have a considerable impact on crop yield. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate resistance genes against the insects Aphis glycines and Spodoptera litura, and the nematode Heterodera glycines. We identified 171, 7, and 228 high-confidence candidate resistance genes against A. glycines, S. litura, and H. glycines, respectively. We found some overlap of candidate genes between insect species, but not between insects and H. glycines. Although 15% of the prioritized candidate genes encode proteins of unknown function, the vast majority of the candidates are related to plant immunity processes, such as transcriptional regulation, signaling, oxidative stress, recognition, and physical defense. Based on the number of resistance alleles, we selected the ten most promising accessions against each pest species in the soybean USDA germplasm. The most resistant accessions do not reach the maximum theoretical resistance potential, indicating that they might be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression networks we inferred in this work are available in a user-friendly web application (https://soypestgcn.venanciogroup.uenf.br/) and an R/Shiny package (https://github.com/almeidasilvaf/SoyPestGCN) that serve as a public resource to explore soybean-pest interactions at the transcriptional level.
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